Overview

Dataset statistics

Number of variables36
Number of observations953
Missing cells25067
Missing cells (%)73.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory268.2 KiB
Average record size in memory288.1 B

Variable types

Categorical18
Numeric18

Alerts

ILC_LI02_unit has constant value ""Constant
ILC_PW01_unit has constant value ""Constant
ILC_PW01_unit_2 has constant value ""Constant
ILC_PW01_unit_3 has constant value ""Constant
ILC_PW01_unit_4 has constant value ""Constant
ILC_PW01_unit_5 has constant value ""Constant
ILC_PW01_unit_6 has constant value ""Constant
ILC_PW01_unit_7 has constant value ""Constant
ILC_PW01_unit_8 has constant value ""Constant
ILC_PW01_unit_9 has constant value ""Constant
ILC_PW01_unit_10 has constant value ""Constant
TOUR_OCC_NINATS_unit has constant value ""Constant
RAIL_TF_PASSMOV_unit has constant value ""Constant
RAIL_PA_TOTAL_unit has constant value ""Constant
RAIL_PA_TOTAL_unit_14 has constant value ""Constant
RAIL_AC_CATNMBR_unit has constant value ""Constant
TTR00003_unit has constant value ""Constant
ILC_LI02_VALUE is highly overall correlated with ILC_PW01_VALUE and 8 other fieldsHigh correlation
ILC_PW01_VALUE is highly overall correlated with ILC_LI02_VALUE and 10 other fieldsHigh correlation
ILC_PW01_VALUE_2 is highly overall correlated with ILC_LI02_VALUE and 10 other fieldsHigh correlation
ILC_PW01_VALUE_3 is highly overall correlated with ILC_LI02_VALUE and 10 other fieldsHigh correlation
ILC_PW01_VALUE_4 is highly overall correlated with ILC_LI02_VALUE and 10 other fieldsHigh correlation
ILC_PW01_VALUE_5 is highly overall correlated with ILC_LI02_VALUE and 9 other fieldsHigh correlation
ILC_PW01_VALUE_6 is highly overall correlated with ILC_PW01_VALUE and 8 other fieldsHigh correlation
ILC_PW01_VALUE_7 is highly overall correlated with ILC_LI02_VALUE and 10 other fieldsHigh correlation
ILC_PW01_VALUE_8 is highly overall correlated with ILC_LI02_VALUE and 10 other fieldsHigh correlation
ILC_PW01_VALUE_9 is highly overall correlated with ILC_LI02_VALUE and 9 other fieldsHigh correlation
ILC_PW01_VALUE_10 is highly overall correlated with ILC_PW01_VALUE and 8 other fieldsHigh correlation
TOUR_OCC_NINATS_VALUE is highly overall correlated with RAIL_TF_PASSMOV_VALUE and 5 other fieldsHigh correlation
RAIL_TF_PASSMOV_VALUE is highly overall correlated with ILC_PW01_VALUE_3 and 6 other fieldsHigh correlation
RAIL_PA_TOTAL_VALUE is highly overall correlated with TOUR_OCC_NINATS_VALUE and 5 other fieldsHigh correlation
RAIL_PA_TOTAL_VALUE_14 is highly overall correlated with TOUR_OCC_NINATS_VALUE and 5 other fieldsHigh correlation
RAIL_AC_CATNMBR_VALUE is highly overall correlated with TOUR_OCC_NINATS_VALUE and 4 other fieldsHigh correlation
TTR00003_VALUE is highly overall correlated with TOUR_OCC_NINATS_VALUE and 5 other fieldsHigh correlation
geo is highly overall correlated with ILC_LI02_VALUE and 10 other fieldsHigh correlation
ILC_LI02_unit has 338 (35.5%) missing valuesMissing
ILC_LI02_VALUE has 338 (35.5%) missing valuesMissing
ILC_PW01_unit has 926 (97.2%) missing valuesMissing
ILC_PW01_VALUE has 926 (97.2%) missing valuesMissing
ILC_PW01_unit_2 has 926 (97.2%) missing valuesMissing
ILC_PW01_VALUE_2 has 926 (97.2%) missing valuesMissing
ILC_PW01_unit_3 has 899 (94.3%) missing valuesMissing
ILC_PW01_VALUE_3 has 899 (94.3%) missing valuesMissing
ILC_PW01_unit_4 has 926 (97.2%) missing valuesMissing
ILC_PW01_VALUE_4 has 926 (97.2%) missing valuesMissing
ILC_PW01_unit_5 has 899 (94.3%) missing valuesMissing
ILC_PW01_VALUE_5 has 899 (94.3%) missing valuesMissing
ILC_PW01_unit_6 has 845 (88.7%) missing valuesMissing
ILC_PW01_VALUE_6 has 845 (88.7%) missing valuesMissing
ILC_PW01_unit_7 has 926 (97.2%) missing valuesMissing
ILC_PW01_VALUE_7 has 926 (97.2%) missing valuesMissing
ILC_PW01_unit_8 has 926 (97.2%) missing valuesMissing
ILC_PW01_VALUE_8 has 926 (97.2%) missing valuesMissing
ILC_PW01_unit_9 has 899 (94.3%) missing valuesMissing
ILC_PW01_VALUE_9 has 899 (94.3%) missing valuesMissing
ILC_PW01_unit_10 has 899 (94.3%) missing valuesMissing
ILC_PW01_VALUE_10 has 899 (94.3%) missing valuesMissing
TOUR_OCC_NINATS_unit has 658 (69.0%) missing valuesMissing
TOUR_OCC_NINATS_VALUE has 658 (69.0%) missing valuesMissing
RAIL_TF_PASSMOV_unit has 170 (17.8%) missing valuesMissing
RAIL_TF_PASSMOV_VALUE has 170 (17.8%) missing valuesMissing
RAIL_PA_TOTAL_unit has 481 (50.5%) missing valuesMissing
RAIL_PA_TOTAL_VALUE has 523 (54.9%) missing valuesMissing
RAIL_PA_TOTAL_unit_14 has 481 (50.5%) missing valuesMissing
RAIL_PA_TOTAL_VALUE_14 has 522 (54.8%) missing valuesMissing
RAIL_AC_CATNMBR_unit has 627 (65.8%) missing valuesMissing
RAIL_AC_CATNMBR_VALUE has 653 (68.5%) missing valuesMissing
TTR00003_unit has 653 (68.5%) missing valuesMissing
TTR00003_VALUE has 653 (68.5%) missing valuesMissing

Reproduction

Analysis started2024-01-10 02:04:20.447493
Analysis finished2024-01-10 02:04:58.881601
Duration38.43 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

ILC_LI02_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing338
Missing (%)35.5%
Memory size7.6 KiB
PC
615 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1230
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPC
2nd rowPC
3rd rowPC
4th rowPC
5th rowPC

Common Values

ValueCountFrequency (%)
PC 615
64.5%
(Missing) 338
35.5%

Length

2024-01-10T02:04:58.951624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:59.055772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
pc 615
100.0%

Most occurring characters

ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1230
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1230
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 615
50.0%
C 615
50.0%

geo
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
SE
 
53
BE
 
44
FR
 
44
DK
 
44
ES
 
43
Other values (22)
725 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1906
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAT
2nd rowAT
3rd rowAT
4th rowAT
5th rowAT

Common Values

ValueCountFrequency (%)
SE 53
 
5.6%
BE 44
 
4.6%
FR 44
 
4.6%
DK 44
 
4.6%
ES 43
 
4.5%
PT 43
 
4.5%
DE 42
 
4.4%
EL 41
 
4.3%
LU 41
 
4.3%
NL 39
 
4.1%
Other values (17) 519
54.5%

Length

2024-01-10T02:04:59.151073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 53
 
5.6%
be 44
 
4.6%
fr 44
 
4.6%
dk 44
 
4.6%
es 43
 
4.5%
pt 43
 
4.5%
de 42
 
4.4%
el 41
 
4.3%
lu 41
 
4.3%
nl 39
 
4.1%
Other values (17) 519
54.5%

Most occurring characters

ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1906
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1906
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 316
16.6%
L 217
11.4%
T 163
 
8.6%
S 160
 
8.4%
I 135
 
7.1%
R 104
 
5.5%
D 86
 
4.5%
F 78
 
4.1%
B 77
 
4.0%
K 75
 
3.9%
Other values (12) 495
26.0%

TIME_PERIOD
Real number (ℝ)

Distinct53
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.7114
Minimum1970
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:59.259679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1982
Q11995
median2005
Q32014
95-th percentile2021
Maximum2022
Range52
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.8875
Coefficient of variation (CV)0.0059327404
Kurtosis-0.73517764
Mean2003.7114
Median Absolute Deviation (MAD)9
Skewness-0.36585019
Sum1909537
Variance141.31265
MonotonicityNot monotonic
2024-01-10T02:04:59.416717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 27
 
2.8%
2007 27
 
2.8%
2015 27
 
2.8%
2014 27
 
2.8%
2013 27
 
2.8%
2012 27
 
2.8%
2011 27
 
2.8%
2010 27
 
2.8%
2009 27
 
2.8%
2008 27
 
2.8%
Other values (43) 683
71.7%
ValueCountFrequency (%)
1970 1
 
0.1%
1971 1
 
0.1%
1972 1
 
0.1%
1973 1
 
0.1%
1974 1
 
0.1%
1975 1
 
0.1%
1976 1
 
0.1%
1977 1
 
0.1%
1978 1
 
0.1%
1979 11
1.2%
ValueCountFrequency (%)
2022 27
2.8%
2021 27
2.8%
2020 27
2.8%
2019 27
2.8%
2018 27
2.8%
2017 27
2.8%
2016 27
2.8%
2015 27
2.8%
2014 27
2.8%
2013 27
2.8%

ILC_LI02_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct145
Distinct (%)23.6%
Missing338
Missing (%)35.5%
Infinite0
Infinite (%)0.0%
Mean16.02
Minimum8
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:04:59.558284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10
Q112.95
median15.5
Q319.4
95-th percentile22.36
Maximum26.4
Range18.4
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation3.9336608
Coefficient of variation (CV)0.24554687
Kurtosis-0.86962146
Mean16.02
Median Absolute Deviation (MAD)3.2
Skewness0.20041356
Sum9852.3
Variance15.473687
MonotonicityNot monotonic
2024-01-10T02:04:59.699531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 22
 
2.3%
12 15
 
1.6%
19 13
 
1.4%
20 13
 
1.4%
15 12
 
1.3%
18 12
 
1.3%
21 12
 
1.3%
13 12
 
1.3%
16 10
 
1.0%
14.8 9
 
0.9%
Other values (135) 485
50.9%
(Missing) 338
35.5%
ValueCountFrequency (%)
8 5
0.5%
8.6 3
 
0.3%
9 4
0.4%
9.1 1
 
0.1%
9.5 2
 
0.2%
9.6 3
 
0.3%
9.7 4
0.4%
9.8 1
 
0.1%
9.9 1
 
0.1%
10 9
0.9%
ValueCountFrequency (%)
26.4 1
 
0.1%
25.9 1
 
0.1%
25.4 1
 
0.1%
25.3 1
 
0.1%
25.1 1
 
0.1%
24.6 1
 
0.1%
23.8 2
0.2%
23.6 2
0.2%
23.5 2
0.2%
23.4 3
0.3%

ILC_PW01_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:04:59.825538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:04:59.935251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

ILC_PW01_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)66.7%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.5
Minimum6
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:00.437577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.6
Q17.25
median7.5
Q37.85
95-th percentile8.37
Maximum8.4
Range2.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.58441292
Coefficient of variation (CV)0.077921723
Kurtosis0.35106647
Mean7.5
Median Absolute Deviation (MAD)0.3
Skewness-0.56439176
Sum202.5
Variance0.34153846
MonotonicityNot monotonic
2024-01-10T02:05:00.550368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7.4 3
 
0.3%
7.5 2
 
0.2%
6.6 2
 
0.2%
8.4 2
 
0.2%
7.3 2
 
0.2%
7.7 2
 
0.2%
7.8 2
 
0.2%
7.6 2
 
0.2%
7.1 1
 
0.1%
6 1
 
0.1%
Other values (8) 8
 
0.8%
(Missing) 926
97.2%
ValueCountFrequency (%)
6 1
 
0.1%
6.6 2
0.2%
6.8 1
 
0.1%
6.9 1
 
0.1%
7.1 1
 
0.1%
7.2 1
 
0.1%
7.3 2
0.2%
7.4 3
0.3%
7.5 2
0.2%
7.6 2
0.2%
ValueCountFrequency (%)
8.4 2
0.2%
8.3 1
0.1%
8.2 1
0.1%
8.1 1
0.1%
8 1
0.1%
7.9 1
0.1%
7.8 2
0.2%
7.7 2
0.2%
7.6 2
0.2%
7.5 2
0.2%

ILC_PW01_unit_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:05:00.657872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:00.767686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

ILC_PW01_VALUE_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)59.3%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.4222222
Minimum5.9
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:00.849154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile6.53
Q17.15
median7.5
Q37.75
95-th percentile8.14
Maximum8.3
Range2.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.53445395
Coefficient of variation (CV)0.072007269
Kurtosis1.3708107
Mean7.4222222
Median Absolute Deviation (MAD)0.3
Skewness-0.85149409
Sum200.4
Variance0.28564103
MonotonicityNot monotonic
2024-01-10T02:05:00.956357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.5 5
 
0.5%
8 3
 
0.3%
7.1 3
 
0.3%
7.2 2
 
0.2%
7.3 2
 
0.2%
7.7 2
 
0.2%
7.9 1
 
0.1%
6.6 1
 
0.1%
7 1
 
0.1%
6.5 1
 
0.1%
Other values (6) 6
 
0.6%
(Missing) 926
97.2%
ValueCountFrequency (%)
5.9 1
 
0.1%
6.5 1
 
0.1%
6.6 1
 
0.1%
7 1
 
0.1%
7.1 3
0.3%
7.2 2
 
0.2%
7.3 2
 
0.2%
7.4 1
 
0.1%
7.5 5
0.5%
7.6 1
 
0.1%
ValueCountFrequency (%)
8.3 1
 
0.1%
8.2 1
 
0.1%
8 3
0.3%
7.9 1
 
0.1%
7.8 1
 
0.1%
7.7 2
 
0.2%
7.6 1
 
0.1%
7.5 5
0.5%
7.4 1
 
0.1%
7.3 2
 
0.2%

ILC_PW01_unit_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:05:01.066203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:01.160307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

ILC_PW01_VALUE_3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)50.0%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean6.1259259
Minimum3.7
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:01.254752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile4.43
Q15.425
median6.15
Q36.9
95-th percentile7.6
Maximum7.6
Range3.9
Interquartile range (IQR)1.475

Descriptive statistics

Standard deviation0.97442488
Coefficient of variation (CV)0.15906573
Kurtosis-0.51933199
Mean6.1259259
Median Absolute Deviation (MAD)0.75
Skewness-0.27892564
Sum330.8
Variance0.94950384
MonotonicityNot monotonic
2024-01-10T02:05:01.380511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5.2 5
 
0.5%
7.6 4
 
0.4%
6.3 4
 
0.4%
5.5 3
 
0.3%
6.9 3
 
0.3%
6 3
 
0.3%
5.4 3
 
0.3%
6.4 2
 
0.2%
7.4 2
 
0.2%
5.7 2
 
0.2%
Other values (17) 23
 
2.4%
(Missing) 899
94.3%
ValueCountFrequency (%)
3.7 1
 
0.1%
4.3 2
 
0.2%
4.5 1
 
0.1%
4.6 1
 
0.1%
5 1
 
0.1%
5.2 5
0.5%
5.4 3
0.3%
5.5 3
0.3%
5.6 1
 
0.1%
5.7 2
 
0.2%
ValueCountFrequency (%)
7.6 4
0.4%
7.5 2
0.2%
7.4 2
0.2%
7.3 1
 
0.1%
7.2 1
 
0.1%
7 2
0.2%
6.9 3
0.3%
6.8 2
0.2%
6.7 1
 
0.1%
6.6 2
0.2%

ILC_PW01_unit_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:05:01.490327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:01.599992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

ILC_PW01_VALUE_4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)70.4%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.1
Minimum5.2
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:01.678764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile5.83
Q16.4
median7.3
Q37.7
95-th percentile8.3
Maximum8.4
Range3.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.89828897
Coefficient of variation (CV)0.12651957
Kurtosis-0.83531185
Mean7.1
Median Absolute Deviation (MAD)0.7
Skewness-0.35518431
Sum191.7
Variance0.80692308
MonotonicityNot monotonic
2024-01-10T02:05:01.788571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7.5 4
 
0.4%
8.3 3
 
0.3%
5.9 2
 
0.2%
7 2
 
0.2%
6.6 2
 
0.2%
7.4 1
 
0.1%
7.9 1
 
0.1%
8.4 1
 
0.1%
6 1
 
0.1%
8.1 1
 
0.1%
Other values (9) 9
 
0.9%
(Missing) 926
97.2%
ValueCountFrequency (%)
5.2 1
0.1%
5.8 1
0.1%
5.9 2
0.2%
6 1
0.1%
6.1 1
0.1%
6.2 1
0.1%
6.6 2
0.2%
6.8 1
0.1%
7 2
0.2%
7.2 1
0.1%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.3 3
0.3%
8.1 1
 
0.1%
7.9 1
 
0.1%
7.8 1
 
0.1%
7.6 1
 
0.1%
7.5 4
0.4%
7.4 1
 
0.1%
7.3 1
 
0.1%
7.2 1
 
0.1%

ILC_PW01_unit_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:05:01.913997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:02.023403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

ILC_PW01_VALUE_5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)29.6%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean7.2851852
Minimum6
Maximum8.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:02.101926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.33
Q17.1
median7.3
Q37.5
95-th percentile8.035
Maximum8.1
Range2.1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.44442348
Coefficient of variation (CV)0.061003731
Kurtosis1.609569
Mean7.2851852
Median Absolute Deviation (MAD)0.2
Skewness-0.7175413
Sum393.4
Variance0.19751223
MonotonicityNot monotonic
2024-01-10T02:05:02.211747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.3 9
 
0.9%
7.5 8
 
0.8%
7 7
 
0.7%
7.2 6
 
0.6%
7.1 4
 
0.4%
8.1 3
 
0.3%
7.7 3
 
0.3%
7.4 3
 
0.3%
8 2
 
0.2%
7.6 2
 
0.2%
Other values (6) 7
 
0.7%
(Missing) 899
94.3%
ValueCountFrequency (%)
6 1
 
0.1%
6.1 1
 
0.1%
6.2 1
 
0.1%
6.4 1
 
0.1%
6.9 2
 
0.2%
7 7
0.7%
7.1 4
0.4%
7.2 6
0.6%
7.3 9
0.9%
7.4 3
 
0.3%
ValueCountFrequency (%)
8.1 3
 
0.3%
8 2
 
0.2%
7.8 1
 
0.1%
7.7 3
 
0.3%
7.6 2
 
0.2%
7.5 8
0.8%
7.4 3
 
0.3%
7.3 9
0.9%
7.2 6
0.6%
7.1 4
0.4%

ILC_PW01_unit_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing845
Missing (%)88.7%
Memory size7.6 KiB
RTG
108 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters324
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 108
 
11.3%
(Missing) 845
88.7%

Length

2024-01-10T02:05:02.321943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:02.438334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 108
100.0%

Most occurring characters

ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 324
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 324
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 108
33.3%
T 108
33.3%
G 108
33.3%

ILC_PW01_VALUE_6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)22.2%
Missing845
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean7.1314815
Minimum4.8
Maximum8.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:02.525061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.8
5-th percentile6.2
Q16.8
median7.2
Q37.5
95-th percentile7.965
Maximum8.1
Range3.3
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.58655467
Coefficient of variation (CV)0.082248643
Kurtosis2.0357311
Mean7.1314815
Median Absolute Deviation (MAD)0.35
Skewness-1.058378
Sum770.2
Variance0.34404638
MonotonicityNot monotonic
2024-01-10T02:05:02.635309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7.1 11
 
1.2%
7.3 9
 
0.9%
7 8
 
0.8%
7.5 8
 
0.8%
7.4 8
 
0.8%
7.2 7
 
0.7%
6.7 6
 
0.6%
6.8 6
 
0.6%
7.6 6
 
0.6%
7.8 5
 
0.5%
Other values (14) 34
 
3.6%
(Missing) 845
88.7%
ValueCountFrequency (%)
4.8 1
 
0.1%
5.4 1
 
0.1%
5.6 1
 
0.1%
5.7 1
 
0.1%
6.1 1
 
0.1%
6.2 3
0.3%
6.3 2
 
0.2%
6.4 2
 
0.2%
6.5 5
0.5%
6.7 6
0.6%
ValueCountFrequency (%)
8.1 2
 
0.2%
8 4
0.4%
7.9 3
 
0.3%
7.8 5
0.5%
7.7 5
0.5%
7.6 6
0.6%
7.5 8
0.8%
7.4 8
0.8%
7.3 9
0.9%
7.2 7
0.7%

ILC_PW01_unit_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:05:02.761070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:02.855528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

ILC_PW01_VALUE_7
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)63.0%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.2
Minimum5.2
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:02.933654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile6
Q16.65
median7.5
Q37.75
95-th percentile8.14
Maximum8.4
Range3.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.79227035
Coefficient of variation (CV)0.11003755
Kurtosis-0.028015787
Mean7.2
Median Absolute Deviation (MAD)0.4
Skewness-0.78582801
Sum194.4
Variance0.62769231
MonotonicityNot monotonic
2024-01-10T02:05:03.046412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.6 4
 
0.4%
7.8 3
 
0.3%
6 2
 
0.2%
7.7 2
 
0.2%
8 2
 
0.2%
6.3 2
 
0.2%
7.2 2
 
0.2%
7.4 1
 
0.1%
7.1 1
 
0.1%
6.5 1
 
0.1%
Other values (7) 7
 
0.7%
(Missing) 926
97.2%
ValueCountFrequency (%)
5.2 1
0.1%
6 2
0.2%
6.2 1
0.1%
6.3 2
0.2%
6.5 1
0.1%
6.8 1
0.1%
6.9 1
0.1%
7.1 1
0.1%
7.2 2
0.2%
7.4 1
0.1%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.2 1
 
0.1%
8 2
0.2%
7.8 3
0.3%
7.7 2
0.2%
7.6 4
0.4%
7.5 1
 
0.1%
7.4 1
 
0.1%
7.2 2
0.2%
7.1 1
 
0.1%

ILC_PW01_unit_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing926
Missing (%)97.2%
Memory size7.6 KiB
RTG
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 27
 
2.8%
(Missing) 926
97.2%

Length

2024-01-10T02:05:03.153594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:03.262975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 27
100.0%

Most occurring characters

ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 27
33.3%
T 27
33.3%
G 27
33.3%

ILC_PW01_VALUE_8
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)55.6%
Missing926
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean7.4814815
Minimum6.1
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:03.344211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile6.72
Q17.3
median7.5
Q37.8
95-th percentile8.07
Maximum8.2
Range2.1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46163342
Coefficient of variation (CV)0.061703477
Kurtosis1.9828632
Mean7.4814815
Median Absolute Deviation (MAD)0.3
Skewness-1.0653139
Sum202
Variance0.21310541
MonotonicityNot monotonic
2024-01-10T02:05:03.453520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.5 4
 
0.4%
7.6 3
 
0.3%
7.4 3
 
0.3%
7.9 2
 
0.2%
8 2
 
0.2%
7 2
 
0.2%
7.8 2
 
0.2%
7.3 2
 
0.2%
6.1 1
 
0.1%
7.2 1
 
0.1%
Other values (5) 5
 
0.5%
(Missing) 926
97.2%
ValueCountFrequency (%)
6.1 1
 
0.1%
6.6 1
 
0.1%
7 2
0.2%
7.1 1
 
0.1%
7.2 1
 
0.1%
7.3 2
0.2%
7.4 3
0.3%
7.5 4
0.4%
7.6 3
0.3%
7.7 1
 
0.1%
ValueCountFrequency (%)
8.2 1
 
0.1%
8.1 1
 
0.1%
8 2
0.2%
7.9 2
0.2%
7.8 2
0.2%
7.7 1
 
0.1%
7.6 3
0.3%
7.5 4
0.4%
7.4 3
0.3%
7.3 2
0.2%

ILC_PW01_unit_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:05:03.561072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:03.671100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

ILC_PW01_VALUE_9
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)31.5%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean7.9296296
Minimum5.7
Maximum8.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:03.751772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.7
5-th percentile7.065
Q17.725
median7.95
Q38.3
95-th percentile8.6
Maximum8.6
Range2.9
Interquartile range (IQR)0.575

Descriptive statistics

Standard deviation0.53363461
Coefficient of variation (CV)0.067296284
Kurtosis4.9311418
Mean7.9296296
Median Absolute Deviation (MAD)0.35
Skewness-1.6572423
Sum428.2
Variance0.2847659
MonotonicityNot monotonic
2024-01-10T02:05:03.846448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.9 7
 
0.7%
7.6 6
 
0.6%
7.8 6
 
0.6%
8.6 5
 
0.5%
8.2 5
 
0.5%
8.5 4
 
0.4%
8.3 4
 
0.4%
8 4
 
0.4%
8.1 3
 
0.3%
8.4 2
 
0.2%
Other values (7) 8
 
0.8%
(Missing) 899
94.3%
ValueCountFrequency (%)
5.7 1
 
0.1%
6.6 1
 
0.1%
7 1
 
0.1%
7.1 1
 
0.1%
7.3 2
 
0.2%
7.5 1
 
0.1%
7.6 6
0.6%
7.7 1
 
0.1%
7.8 6
0.6%
7.9 7
0.7%
ValueCountFrequency (%)
8.6 5
0.5%
8.5 4
0.4%
8.4 2
 
0.2%
8.3 4
0.4%
8.2 5
0.5%
8.1 3
0.3%
8 4
0.4%
7.9 7
0.7%
7.8 6
0.6%
7.7 1
 
0.1%

ILC_PW01_unit_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.9%
Missing899
Missing (%)94.3%
Memory size7.6 KiB
RTG
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRTG
2nd rowRTG
3rd rowRTG
4th rowRTG
5th rowRTG

Common Values

ValueCountFrequency (%)
RTG 54
 
5.7%
(Missing) 899
94.3%

Length

2024-01-10T02:05:03.969729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:04.079541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rtg 54
100.0%

Most occurring characters

ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 162
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54
33.3%
T 54
33.3%
G 54
33.3%

ILC_PW01_VALUE_10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)33.3%
Missing899
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean6.8166667
Minimum5.5
Maximum7.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:04.158024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile5.96
Q16.6
median6.8
Q37.1
95-th percentile7.57
Maximum7.8
Range2.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4913401
Coefficient of variation (CV)0.072079232
Kurtosis0.59476157
Mean6.8166667
Median Absolute Deviation (MAD)0.25
Skewness-0.34705153
Sum368.1
Variance0.24141509
MonotonicityNot monotonic
2024-01-10T02:05:04.267832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6.8 6
 
0.6%
6.6 6
 
0.6%
6.9 6
 
0.6%
6.7 5
 
0.5%
7 4
 
0.4%
7.2 3
 
0.3%
6.4 3
 
0.3%
7.3 3
 
0.3%
7.5 3
 
0.3%
6.3 2
 
0.2%
Other values (8) 13
 
1.4%
(Missing) 899
94.3%
ValueCountFrequency (%)
5.5 1
 
0.1%
5.7 2
 
0.2%
6.1 2
 
0.2%
6.3 2
 
0.2%
6.4 3
0.3%
6.5 2
 
0.2%
6.6 6
0.6%
6.7 5
0.5%
6.8 6
0.6%
6.9 6
0.6%
ValueCountFrequency (%)
7.8 2
 
0.2%
7.7 1
 
0.1%
7.5 3
0.3%
7.4 1
 
0.1%
7.3 3
0.3%
7.2 3
0.3%
7.1 2
 
0.2%
7 4
0.4%
6.9 6
0.6%
6.8 6
0.6%

TOUR_OCC_NINATS_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing658
Missing (%)69.0%
Memory size7.6 KiB
NR
295 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNR
2nd rowNR
3rd rowNR
4th rowNR
5th rowNR

Common Values

ValueCountFrequency (%)
NR 295
31.0%
(Missing) 658
69.0%

Length

2024-01-10T02:05:04.377957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:04.487763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nr 295
100.0%

Most occurring characters

ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 590
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 590
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 295
50.0%
R 295
50.0%

TOUR_OCC_NINATS_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct295
Distinct (%)100.0%
Missing658
Missing (%)69.0%
Infinite0
Infinite (%)0.0%
Mean57049184
Minimum840359
Maximum3.429956 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:04.581839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum840359
5-th percentile2782589.6
Q18987021
median21232963
Q351118864
95-th percentile2.7643455 × 108
Maximum3.429956 × 108
Range3.4215524 × 108
Interquartile range (IQR)42131842

Descriptive statistics

Standard deviation84602426
Coefficient of variation (CV)1.4829735
Kurtosis2.8915679
Mean57049184
Median Absolute Deviation (MAD)15058225
Skewness2.0332376
Sum1.6829509 × 1010
Variance7.1575705 × 1015
MonotonicityNot monotonic
2024-01-10T02:05:04.723314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3845346 1
 
0.1%
3374068 1
 
0.1%
3307837 1
 
0.1%
2954395 1
 
0.1%
2842382 1
 
0.1%
1596490 1
 
0.1%
1032999 1
 
0.1%
840359 1
 
0.1%
1654054 1
 
0.1%
1714113 1
 
0.1%
Other values (285) 285
29.9%
(Missing) 658
69.0%
ValueCountFrequency (%)
840359 1
0.1%
1032999 1
0.1%
1541786 1
0.1%
1569926 1
0.1%
1596490 1
0.1%
1654054 1
0.1%
1693749 1
0.1%
1698773 1
0.1%
1714113 1
0.1%
1738110 1
0.1%
ValueCountFrequency (%)
342995595 1
0.1%
340577818 1
0.1%
339980928 1
0.1%
331168945 1
0.1%
320366108 1
0.1%
308235728 1
0.1%
306848903 1
0.1%
297554891 1
0.1%
295260630 1
0.1%
288759266 1
0.1%

RAIL_TF_PASSMOV_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing170
Missing (%)17.8%
Memory size7.6 KiB
THS_TRKM
783 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6264
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHS_TRKM
2nd rowTHS_TRKM
3rd rowTHS_TRKM
4th rowTHS_TRKM
5th rowTHS_TRKM

Common Values

ValueCountFrequency (%)
THS_TRKM 783
82.2%
(Missing) 170
 
17.8%

Length

2024-01-10T02:05:04.864991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:04.974822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ths_trkm 783
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1566
25.0%
H 783
12.5%
S 783
12.5%
_ 783
12.5%
R 783
12.5%
K 783
12.5%
M 783
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5481
87.5%
Connector Punctuation 783
 
12.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1566
28.6%
H 783
14.3%
S 783
14.3%
R 783
14.3%
K 783
14.3%
M 783
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5481
87.5%
Common 783
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1566
28.6%
H 783
14.3%
S 783
14.3%
R 783
14.3%
K 783
14.3%
M 783
14.3%
Common
ValueCountFrequency (%)
_ 783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1566
25.0%
H 783
12.5%
S 783
12.5%
_ 783
12.5%
R 783
12.5%
K 783
12.5%
M 783
12.5%

RAIL_TF_PASSMOV_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct777
Distinct (%)99.2%
Missing170
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean106326.38
Minimum2201
Maximum1090772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:05.084490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2201
5-th percentile5303.8
Q115470.5
median61249
Q3123545.5
95-th percentile391778.1
Maximum1090772
Range1088571
Interquartile range (IQR)108075

Descriptive statistics

Standard deviation154716.76
Coefficient of variation (CV)1.4551117
Kurtosis12.997458
Mean106326.38
Median Absolute Deviation (MAD)48004
Skewness3.2276455
Sum83253553
Variance2.3937275 × 1010
MonotonicityNot monotonic
2024-01-10T02:05:05.225548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62068 2
 
0.2%
22150 2
 
0.2%
27105 2
 
0.2%
41610 2
 
0.2%
61200 2
 
0.2%
11939 2
 
0.2%
5480 1
 
0.1%
5426 1
 
0.1%
41350 1
 
0.1%
41430 1
 
0.1%
Other values (767) 767
80.5%
(Missing) 170
 
17.8%
ValueCountFrequency (%)
2201 1
0.1%
2714 1
0.1%
2822 1
0.1%
2838 1
0.1%
2858 1
0.1%
2884 1
0.1%
2893 1
0.1%
2914 1
0.1%
2990 1
0.1%
3036 1
0.1%
ValueCountFrequency (%)
1090772 1
0.1%
1080000 1
0.1%
1079700 1
0.1%
1079000 1
0.1%
848000 1
0.1%
811100 1
0.1%
809000 1
0.1%
808000 1
0.1%
806000 1
0.1%
799000 1
0.1%

RAIL_PA_TOTAL_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
MIO_PKM
472 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3304
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIO_PKM
2nd rowMIO_PKM
3rd rowMIO_PKM
4th rowMIO_PKM
5th rowMIO_PKM

Common Values

ValueCountFrequency (%)
MIO_PKM 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:05:05.355173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:05.447879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mio_pkm 472
100.0%

Most occurring characters

ValueCountFrequency (%)
M 944
28.6%
I 472
14.3%
O 472
14.3%
_ 472
14.3%
P 472
14.3%
K 472
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2832
85.7%
Connector Punctuation 472
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 944
33.3%
I 472
16.7%
O 472
16.7%
P 472
16.7%
K 472
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2832
85.7%
Common 472
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 944
33.3%
I 472
16.7%
O 472
16.7%
P 472
16.7%
K 472
16.7%
Common
ValueCountFrequency (%)
_ 472
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 944
28.6%
I 472
14.3%
O 472
14.3%
_ 472
14.3%
P 472
14.3%
K 472
14.3%

RAIL_PA_TOTAL_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct414
Distinct (%)96.3%
Missing523
Missing (%)54.9%
Infinite0
Infinite (%)0.0%
Mean13940.24
Minimum193
Maximum102814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:05.555418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum193
5-th percentile274
Q1962.25
median4147.5
Q311688.75
95-th percentile82652.95
Maximum102814
Range102621
Interquartile range (IQR)10726.5

Descriptive statistics

Standard deviation24157.281
Coefficient of variation (CV)1.7329172
Kurtosis4.548878
Mean13940.24
Median Absolute Deviation (MAD)3612.5
Skewness2.3758848
Sum5994303
Variance5.8357423 × 108
MonotonicityNot monotonic
2024-01-10T02:05:05.704807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 3
 
0.3%
3876 2
 
0.2%
382 2
 
0.2%
268 2
 
0.2%
4271 2
 
0.2%
9403 2
 
0.2%
278 2
 
0.2%
941 2
 
0.2%
724 2
 
0.2%
3957 2
 
0.2%
Other values (404) 409
42.9%
(Missing) 523
54.9%
ValueCountFrequency (%)
193 1
0.1%
223 1
0.1%
231 1
0.1%
235 1
0.1%
237 1
0.1%
243 1
0.1%
244 1
0.1%
246 1
0.1%
247 1
0.1%
248 1
0.1%
ValueCountFrequency (%)
102814 1
0.1%
100252 1
0.1%
98161 1
0.1%
96540 1
0.1%
95529 1
0.1%
95465 1
0.1%
95024 1
0.1%
93918 1
0.1%
92313 1
0.1%
91832 1
0.1%

RAIL_PA_TOTAL_unit_14
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing481
Missing (%)50.5%
Memory size7.6 KiB
THS_PAS
472 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3304
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHS_PAS
2nd rowTHS_PAS
3rd rowTHS_PAS
4th rowTHS_PAS
5th rowTHS_PAS

Common Values

ValueCountFrequency (%)
THS_PAS 472
49.5%
(Missing) 481
50.5%

Length

2024-01-10T02:05:05.815143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:05.924737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ths_pas 472
100.0%

Most occurring characters

ValueCountFrequency (%)
S 944
28.6%
T 472
14.3%
H 472
14.3%
_ 472
14.3%
P 472
14.3%
A 472
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2832
85.7%
Connector Punctuation 472
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 944
33.3%
T 472
16.7%
H 472
16.7%
P 472
16.7%
A 472
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2832
85.7%
Common 472
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 944
33.3%
T 472
16.7%
H 472
16.7%
P 472
16.7%
A 472
16.7%
Common
ValueCountFrequency (%)
_ 472
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 944
28.6%
T 472
14.3%
H 472
14.3%
_ 472
14.3%
P 472
14.3%
A 472
14.3%

RAIL_PA_TOTAL_VALUE_14
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct430
Distinct (%)99.8%
Missing522
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean286126.39
Minimum3238
Maximum2938023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:06.018940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3238
5-th percentile5216.5
Q120345.5
median77265
Q3233303
95-th percentile1240802
Maximum2938023
Range2934785
Interquartile range (IQR)212957.5

Descriptive statistics

Standard deviation536890.47
Coefficient of variation (CV)1.8764102
Kurtosis10.461147
Mean286126.39
Median Absolute Deviation (MAD)71114
Skewness3.1907096
Sum1.2332047 × 108
Variance2.8825138 × 1011
MonotonicityNot monotonic
2024-01-10T02:05:06.160081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21329 2
 
0.2%
20804 1
 
0.1%
21504 1
 
0.1%
26702 1
 
0.1%
27380 1
 
0.1%
27387 1
 
0.1%
25915 1
 
0.1%
22038 1
 
0.1%
16595 1
 
0.1%
14527 1
 
0.1%
Other values (420) 420
44.1%
(Missing) 522
54.8%
ValueCountFrequency (%)
3238 1
0.1%
3790 1
0.1%
3795 1
0.1%
3819 1
0.1%
3916 1
0.1%
3948 1
0.1%
4126 1
0.1%
4127 1
0.1%
4176 1
0.1%
4199 1
0.1%
ValueCountFrequency (%)
2938023 1
0.1%
2880558 1
0.1%
2831443 1
0.1%
2813782 1
0.1%
2693080 1
0.1%
2684908 1
0.1%
2612764 1
0.1%
2564498 1
0.1%
2530284 1
0.1%
2505856 1
0.1%

RAIL_AC_CATNMBR_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing627
Missing (%)65.8%
Memory size7.6 KiB
NR
326 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters652
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNR
2nd rowNR
3rd rowNR
4th rowNR
5th rowNR

Common Values

ValueCountFrequency (%)
NR 326
34.2%
(Missing) 627
65.8%

Length

2024-01-10T02:05:06.301168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:06.411282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nr 326
100.0%

Most occurring characters

ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 652
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 652
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 326
50.0%
R 326
50.0%

RAIL_AC_CATNMBR_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct160
Distinct (%)53.3%
Missing653
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean139.66
Minimum0
Maximum2198
Zeros9
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:06.521099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.95
Q128
median62.5
Q3132.25
95-th percentile626.65
Maximum2198
Range2198
Interquartile range (IQR)104.25

Descriptive statistics

Standard deviation251.41621
Coefficient of variation (CV)1.800202
Kurtosis25.085715
Mean139.66
Median Absolute Deviation (MAD)40.5
Skewness4.4313355
Sum41898
Variance63210.111
MonotonicityNot monotonic
2024-01-10T02:05:06.649399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.9%
48 7
 
0.7%
42 6
 
0.6%
33 5
 
0.5%
1 5
 
0.5%
27 5
 
0.5%
39 4
 
0.4%
28 4
 
0.4%
40 4
 
0.4%
94 4
 
0.4%
Other values (150) 247
 
25.9%
(Missing) 653
68.5%
ValueCountFrequency (%)
0 9
0.9%
1 5
0.5%
2 1
 
0.1%
3 2
 
0.2%
4 3
 
0.3%
5 4
0.4%
7 1
 
0.1%
11 3
 
0.3%
12 3
 
0.3%
13 3
 
0.3%
ValueCountFrequency (%)
2198 1
0.1%
1863 1
0.1%
1172 1
0.1%
1150 1
0.1%
1111 1
0.1%
976 1
0.1%
964 1
0.1%
961 1
0.1%
905 1
0.1%
883 1
0.1%

TTR00003_unit
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing653
Missing (%)68.5%
Memory size7.6 KiB
KM
300 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKM
2nd rowKM
3rd rowKM
4th rowKM
5th rowKM

Common Values

ValueCountFrequency (%)
KM 300
31.5%
(Missing) 653
68.5%

Length

2024-01-10T02:05:06.772402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T02:05:06.882453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
km 300
100.0%

Most occurring characters

ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 600
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 300
50.0%
M 300
50.0%

TTR00003_VALUE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct202
Distinct (%)67.3%
Missing653
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean8103.4836
Minimum263
Maximum39068.117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-01-10T02:05:06.992268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum263
5-th percentile1163.85
Q12240
median3627
Q310774.75
95-th percentile29617.15
Maximum39068.117
Range38805.117
Interquartile range (IQR)8534.75

Descriptive statistics

Standard deviation9288.367
Coefficient of variation (CV)1.146219
Kurtosis3.1590706
Mean8103.4836
Median Absolute Deviation (MAD)2299
Skewness1.9026826
Sum2431045.1
Variance86273761
MonotonicityNot monotonic
2024-01-10T02:05:07.133124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1209 10
 
1.0%
1839 7
 
0.7%
275 6
 
0.6%
3626 5
 
0.5%
2604 5
 
0.5%
2045 5
 
0.5%
5944 4
 
0.4%
1859.6 4
 
0.4%
1767.6 4
 
0.4%
263 4
 
0.4%
Other values (192) 246
 
25.8%
(Missing) 653
68.5%
ValueCountFrequency (%)
263 4
0.4%
271 2
 
0.2%
275 6
0.6%
1161 3
0.3%
1164 1
 
0.1%
1166 2
 
0.2%
1167 3
0.3%
1175 1
 
0.1%
1196 2
 
0.2%
1208 1
 
0.1%
ValueCountFrequency (%)
39068.117 1
0.1%
38994.798 1
0.1%
38836.096 1
0.1%
38802.643 1
0.1%
38799.773 1
0.1%
38783.143 1
0.1%
38712 1
0.1%
38696.542 1
0.1%
38658.765 1
0.1%
38653.801 1
0.1%

Interactions

2024-01-10T02:04:54.871402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:21.137304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:23.120487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:25.121689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:26.925864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:28.678250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:30.580359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:32.424040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:34.184927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:36.018762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:37.854599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:41.364990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:43.175711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:44.911376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:46.849499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:48.830194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:51.037530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:53.002489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:54.971022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:21.262456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:23.240371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:25.223346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:27.032337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:28.794059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:30.695914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:32.519187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:34.293808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:36.134227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:37.955372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:41.457602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:43.277122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:45.026449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:46.962058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:48.952920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:51.151955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:53.122223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:55.086730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:21.384002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:23.368742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:25.324197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:27.125529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:28.911151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:30.796872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:32.624141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:34.399251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:36.245071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:38.062942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:41.573248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:43.375660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:45.146632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:47.077904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:49.065217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:51.265679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:53.234996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:55.203672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:21.488412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:23.470879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:25.423576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:27.225724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:29.011415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:30.908026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:32.715307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:34.500669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:36.336342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:38.154819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:41.673739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:43.473978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:45.252527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:47.196102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:49.151186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:51.368403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:53.334022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:55.288307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:21.585801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:23.553949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:25.523683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:27.321447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:29.095665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:30.996158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:32.807995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:34.583982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:36.421308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:38.244187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:41.759235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:43.558004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:45.352783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:47.296162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:49.251814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:51.467951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:53.434258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:55.405649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:21.688672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:23.670579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:25.624566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:27.428145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:29.217691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:31.096028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:32.912912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:34.702379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:36.542063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:38.336780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:41.856682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:43.660222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:45.460592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:47.423017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:49.362469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:51.582363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:53.533113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:55.521674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:21.803476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:23.772674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:25.724772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:27.527096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:29.312348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:31.197769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-01-10T02:04:39.489194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:43.057952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:44.809803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:46.728762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:48.713219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:50.916737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:52.893357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-01-10T02:04:54.753665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-01-10T02:05:07.274244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
TIME_PERIODILC_LI02_VALUEILC_PW01_VALUEILC_PW01_VALUE_2ILC_PW01_VALUE_3ILC_PW01_VALUE_4ILC_PW01_VALUE_5ILC_PW01_VALUE_6ILC_PW01_VALUE_7ILC_PW01_VALUE_8ILC_PW01_VALUE_9ILC_PW01_VALUE_10TOUR_OCC_NINATS_VALUERAIL_TF_PASSMOV_VALUERAIL_PA_TOTAL_VALUERAIL_PA_TOTAL_VALUE_14RAIL_AC_CATNMBR_VALUETTR00003_VALUEgeo
TIME_PERIOD1.0000.149NaNNaN0.225NaN0.0870.099NaNNaN0.1060.025-0.029-0.021-0.068-0.062-0.267-0.0030.064
ILC_LI02_VALUE0.1491.000-0.721-0.615-0.528-0.544-0.510-0.457-0.501-0.556-0.551-0.487-0.014-0.255-0.236-0.276-0.014-0.0940.506
ILC_PW01_VALUENaN-0.7211.0000.7820.8100.7300.7800.8610.7380.8200.7990.757-0.0800.4320.1970.308-0.1350.0281.000
ILC_PW01_VALUE_2NaN-0.6150.7821.0000.6640.8460.7470.7580.8690.7190.7250.803-0.1060.217-0.0330.099-0.336-0.2311.000
ILC_PW01_VALUE_30.225-0.5280.8100.6641.0000.7850.7660.9440.7640.6680.5570.7890.1040.5730.4250.4900.0750.2260.220
ILC_PW01_VALUE_4NaN-0.5440.7300.8460.7851.0000.7670.8460.8920.7640.7430.814-0.1160.3650.1070.191-0.161-0.0241.000
ILC_PW01_VALUE_50.087-0.5100.7800.7470.7660.7671.0000.7710.6980.7890.7290.868-0.2610.054-0.0050.059-0.308-0.1650.484
ILC_PW01_VALUE_60.099-0.4570.8610.7580.9440.8460.7711.0000.8400.7840.6620.8250.0640.4360.2690.307-0.0590.1270.369
ILC_PW01_VALUE_7NaN-0.5010.7380.8690.7640.8920.6980.8401.0000.7210.7180.776-0.0730.3350.0390.155-0.200-0.1221.000
ILC_PW01_VALUE_8NaN-0.5560.8200.7190.6680.7640.7890.7840.7211.0000.8240.689-0.2580.284-0.0580.085-0.358-0.2501.000
ILC_PW01_VALUE_90.106-0.5510.7990.7250.5570.7430.7290.6620.7180.8241.0000.605-0.1040.1440.1220.183-0.260-0.0990.386
ILC_PW01_VALUE_100.025-0.4870.7570.8030.7890.8140.8680.8250.7760.6890.6051.000-0.1720.1320.0600.134-0.271-0.1180.398
TOUR_OCC_NINATS_VALUE-0.029-0.014-0.080-0.1060.104-0.116-0.2610.064-0.073-0.258-0.104-0.1721.0000.7780.8330.7750.5580.7100.565
RAIL_TF_PASSMOV_VALUE-0.021-0.2550.4320.2170.5730.3650.0540.4360.3350.2840.1440.1320.7781.0000.9810.9620.6070.9010.519
RAIL_PA_TOTAL_VALUE-0.068-0.2360.197-0.0330.4250.107-0.0050.2690.039-0.0580.1220.0600.8330.9811.0000.9730.6310.9110.637
RAIL_PA_TOTAL_VALUE_14-0.062-0.2760.3080.0990.4900.1910.0590.3070.1550.0850.1830.1340.7750.9620.9731.0000.5700.8400.580
RAIL_AC_CATNMBR_VALUE-0.267-0.014-0.135-0.3360.075-0.161-0.308-0.059-0.200-0.358-0.260-0.2710.5580.6070.6310.5701.0000.8370.416
TTR00003_VALUE-0.003-0.0940.028-0.2310.226-0.024-0.1650.127-0.122-0.250-0.099-0.1180.7100.9010.9110.8400.8371.0000.850
geo0.0640.5061.0001.0000.2201.0000.4840.3691.0001.0000.3860.3980.5650.5190.6370.5800.4160.8501.000

Missing values

2024-01-10T02:04:56.940463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T02:04:57.507798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-10T02:04:58.105237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ILC_LI02_unitgeoTIME_PERIODILC_LI02_VALUEILC_PW01_unitILC_PW01_VALUEILC_PW01_unit_2ILC_PW01_VALUE_2ILC_PW01_unit_3ILC_PW01_VALUE_3ILC_PW01_unit_4ILC_PW01_VALUE_4ILC_PW01_unit_5ILC_PW01_VALUE_5ILC_PW01_unit_6ILC_PW01_VALUE_6ILC_PW01_unit_7ILC_PW01_VALUE_7ILC_PW01_unit_8ILC_PW01_VALUE_8ILC_PW01_unit_9ILC_PW01_VALUE_9ILC_PW01_unit_10ILC_PW01_VALUE_10TOUR_OCC_NINATS_unitTOUR_OCC_NINATS_VALUERAIL_TF_PASSMOV_unitRAIL_TF_PASSMOV_VALUERAIL_PA_TOTAL_unitRAIL_PA_TOTAL_VALUERAIL_PA_TOTAL_unit_14RAIL_PA_TOTAL_VALUE_14RAIL_AC_CATNMBR_unitRAIL_AC_CATNMBR_VALUETTR00003_unitTTR00003_VALUE
0PCAT199513.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM88693.0NaNNaNNaNNaNNaNNaNNaNNaN
1PCAT199614.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM82912.0NaNNaNNaNNaNNaNNaNNaNNaN
2PCAT199713.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM86922.0NaNNaNNaNNaNNaNNaNNaNNaN
3PCAT199813.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM90228.0NaNNaNNaNNaNNaNNaNNaNNaN
4PCAT199912.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5PCAT200012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6PCAT200112.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7PCAT200313.2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8PCAT200413.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM93903.0MIO_PKM8274.0THS_PAS215083.0NR119.0NaNNaN
9PCAT200512.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM94757.0MIO_PKM8685.0THS_PAS220116.0NR98.0NaNNaN
ILC_LI02_unitgeoTIME_PERIODILC_LI02_VALUEILC_PW01_unitILC_PW01_VALUEILC_PW01_unit_2ILC_PW01_VALUE_2ILC_PW01_unit_3ILC_PW01_VALUE_3ILC_PW01_unit_4ILC_PW01_VALUE_4ILC_PW01_unit_5ILC_PW01_VALUE_5ILC_PW01_unit_6ILC_PW01_VALUE_6ILC_PW01_unit_7ILC_PW01_VALUE_7ILC_PW01_unit_8ILC_PW01_VALUE_8ILC_PW01_unit_9ILC_PW01_VALUE_9ILC_PW01_unit_10ILC_PW01_VALUE_10TOUR_OCC_NINATS_unitTOUR_OCC_NINATS_VALUERAIL_TF_PASSMOV_unitRAIL_TF_PASSMOV_VALUERAIL_PA_TOTAL_unitRAIL_PA_TOTAL_VALUERAIL_PA_TOTAL_unit_14RAIL_PA_TOTAL_VALUE_14RAIL_AC_CATNMBR_unitRAIL_AC_CATNMBR_VALUETTR00003_unitTTR00003_VALUE
943NaNSK1999NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM36606.0NaNNaNNaNNaNNaNNaNNaNNaN
944NaNSK2000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM35857.0NaNNaNNaNNaNNaNNaNNaNNaN
945NaNSK2001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM34942.0NaNNaNNaNNaNNaNNaNNaNNaN
946NaNSK2002NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM35727.0NaNNaNNaNNaNNaNNaNNaNNaN
947NaNSK2003NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM30680.0NaNNaNNaNNaNNaNNaNNaNNaN
948NaNSK2004NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTHS_TRKM31228.0MIO_PKM2228.0THS_PAS50325.0NR514.0NaNNaN
949NaNDE2004NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMIO_PKM75903.0THS_PAS2091268.0NR1172.0NaNNaN
950NaNNL2004NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMIO_PKM14509.0THS_PAS326953.0NR37.0NaNNaN
951NaNCY2004NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNRNaNNaNNaN
952NaNMT2004NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNRNaNNaNNaN